A feature selection technique along with an information fusion procedure for improving the
recognition accuracy of a visual and thermal image-based facial recognition system is presented in this study.
A novel modular Kernel Eigen spaces approach is developed and implemented on the phase congruency feature
maps extracted from the visual and thermal images individually. This study proposes a novel face recognition
method which exploits both global and local discriminative features. In this method, global features are
extracted from the whole face images by keeping the low-frequency coefficients of fourier transform, which
we believe encodes the holistic facial Information, such as facial contour. For local feature extraction, Gabor
wavelets are exploited considering their biological relevance. After that, to the global fourier features and each
local patch of Gabor features.